Machine translation enables automatic translation from one language into another language. With development of economic globalization, information exchange between countries using different languages becomes increasingly frequent, and quickly acquiring multilingual information and resources by means of machine translation has become an inevitable trend.
In a process of machine translation, semantic similarity evaluation performed on sentences in different languages helps to obtain a translated text that is more accurately translated. In addition, the machine translation cannot provide translated text that is totally accurate, and a sentence that requires manual modification can be quickly located in the translated text by means of semantic similarity evaluation.
In the prior art, semantic similarity evaluation is mainly based on external resource matching. For a sentence obtained through translation, a semantic similarity between the sentence obtained through translation and a source sentence is evaluated by querying an external resource, where the external resource may be a dictionary or an existing scheme for bilingual matching. However, the foregoing semantic similarity evaluation is word-based semantic evaluation, without regard to a word order, and therefore, the evaluation is inaccurate. For example, a source sentence is “” in Chinese, and a sentence obtained through translation is “morning good” in English. It may be known by consulting a dictionary that semantic meanings of two words “” and “morning” are consistent and that semantic meanings of “” and “good” are consistent; as a result, in the prior art, semantic meanings of the two sentences “” and “morning good” are evaluated as consistent, leading to a mistake in semantic similarity evaluation.